With the increased use of medical imaging, particularly computed tomography (CT), there is an increasing awareness of the possible population risk associated with CT radiation. It is, now more than ever, necessary to justify an imaging exam to ensure that a particular technology is effectively utilized for the overall benefit of the patient and also that any potential harm is minimized. Such a goal is ideally achieved through clinical trials. However, it is exceedingly impractical and costly to attempt a clinical trial for the many new nuances of an increasing number of technological offerings in CT. Virtual clinical trials can address this growing, critical need. Such trials can provide results quickly and cost effectively prior to clinical implementation. They can be used as a precursor to more targeted clinical trials or as their replacement if a sufficient level of realism is achieved. Virtual clinical trials require a virtual patient population. In our prior funded project, we developed a population of 400 computational 4D XCAT whole-body human models capable of simulating a wide range of anatomical variations across representative ages, genders, and body habitus. The models have been used in numerous research projects, specifically for the optimization of nuclear medicine modalities and CT dosimetry. Despite this progress, the current XCAT phantom library is totally inadequate for the realistic optimization of CT imaging devices and protocols as tissue heterogeneities and perfusion have remained un-modeled. Without these, the phantoms cannot be used to assess image quality. The simulated images are far too unrealistic to be representative of actual patients, lacking intra-organ variability and contrast dynamics. With the growing use of CT, the need for virtual clinical trials is at an all-time high to optimize image quality versus dose. In this study, we plan to address these limitations and create a complete framework for developing virtual clinical trials for CT. Specifically, we will develop the next generation of XCAT phantoms including anatomical textures and lesions to model tissue heterogeneity within the organs (Aim 1). This will result in the first library of phantoms capable of simulating patient quality CT data suitable to conduct studies to investigate diagnostic quality as well as dosimetry. We will further expand the XCAT models to include the dynamics of blood flow and thus the perfusion of contrast media (Aim 2). Over 60% of CT imaging involves the use of contrast agents, which despite its profound impact on dose and image quality, have not been incorporated in most optimization studies. The final aim of the project is to develop an integrated approach to combine the XCAT population with realistic and efficient methods with which to generate and analyze the imaging data, thus creating the first practical platform for virtual trials (Aim 3). The end result of this project will be a comprehensive suite of models and an initial set of tools, available to the imaging community, to run virtual clinical trials in CT, making possible the technological evaluations and optimization that would not be feasible using real human subjects and observers.